

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Tuple 							= 3_8_4
			a_						:						List[Any] 							= 7
			if "tiny" in model_name:
						a_						:						Dict 							= 9_6
						a_						:						List[str] 							= (2, 2, 6, 2)
						a_						:						Union[str, Any] 							= (3, 6, 1_2, 2_4)
			elif "small" in model_name:
						a_						:						int 							= 9_6
						a_						:						Union[str, Any] 							= (2, 2, 1_8, 2)
						a_						:						Dict 							= (3, 6, 1_2, 2_4)
			elif "base" in model_name:
						a_						:						Union[str, Any] 							= 1_2_8
						a_						:						Tuple 							= (2, 2, 1_8, 2)
						a_						:						Optional[Any] 							= (4, 8, 1_6, 3_2)
						a_						:						List[Any] 							= 1_2
						a_						:						List[Any] 							= 5_1_2
			elif "large" in model_name:
						a_						:						Tuple 							= 1_9_2
						a_						:						Optional[Any] 							= (2, 2, 1_8, 2)
						a_						:						Dict 							= (6, 1_2, 2_4, 4_8)
						a_						:						List[Any] 							= 1_2
						a_						:						Optional[Any] 							= 7_6_8

			# set label information
			a_						:						int 							= 1_5_0
			a_						:						str 							= """huggingface/label-files"""
			a_						:						Union[str, Any] 							= """ade20k-id2label.json"""
			a_						:						int 							= json.load(open(hf_hub_download(a__  ,	a__  ,	repo_type="""dataset""")  ,	"""r"""))
			a_						:						Any 							= {int(a__): v for k, v in idalabel.items()}
			a_						:						Tuple 							= {v: k for k, v in idalabel.items()}

			a_						:						Dict 							= SwinConfig(
			    embed_dim=a__  ,	depths=a__  ,	num_heads=a__  ,	window_size=a__  ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  ,	)
			a_						:						Optional[int] 							= UperNetConfig(
			    backbone_config=a__  ,	auxiliary_in_channels=a__  ,	num_labels=a__  ,	idalabel=a__  ,	labelaid=a__  ,	)

			return config


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= []

			# fmt: off
			# stem
			rename_keys.append(("""backbone.patch_embed.projection.weight""", """backbone.embeddings.patch_embeddings.projection.weight"""))
			rename_keys.append(("""backbone.patch_embed.projection.bias""", """backbone.embeddings.patch_embeddings.projection.bias"""))
			rename_keys.append(("""backbone.patch_embed.norm.weight""", """backbone.embeddings.norm.weight"""))
			rename_keys.append(("""backbone.patch_embed.norm.bias""", """backbone.embeddings.norm.bias"""))
			# stages
			for i in range(len(config.backbone_config.depths)):
						for j in range(config.backbone_config.depths[i]):
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias'''))

						if i < 3:
									rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight'''))
									rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias'''))
						rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight'''))
						rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias'''))

			# decode head
			rename_keys.extend(
			    [
			        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
			        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
			        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
			        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
			    ])
			# fmt: on

			return rename_keys


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= dct.pop(a__)
			a_						:						List[str] 							= val


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Dict 							= [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
			for i in range(len(backbone_config.depths)):
						a_						:						int 							= num_features[i]
						for j in range(backbone_config.depths[i]):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									a_						:						Tuple 							= state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight''')
									a_						:						List[Any] 							= state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias''')
									# next, add query, keys and values (in that order) to the state dict
									a_						:						Any 							= in_proj_weight[:dim, :]
									a_						:						List[str] 							= in_proj_bias[: dim]
									a_						:						Tuple 							= in_proj_weight[
									    dim : dim * 2, :
									]
									a_						:						Optional[int] 							= in_proj_bias[
									    dim : dim * 2
									]
									a_						:						int 							= in_proj_weight[
									    -dim :, :
									]
									a_						:						Optional[Any] 							= in_proj_bias[-dim :]
									# fmt: on


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						Union[str, Any] 							= x.shape
			a_						:						Tuple 							= x.reshape(a__  ,	4  ,	in_channel // 4)
			a_						:						Optional[int] 							= x[:, [0, 2, 1, 3], :].transpose(1  ,	2).reshape(a__  ,	a__)
			return x


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						Optional[Any] 							= x.shape
			a_						:						Optional[int] 							= x.reshape(a__  ,	in_channel // 4  ,	4)
			a_						:						Dict 							= x[:, :, [0, 2, 1, 3]].transpose(1  ,	2).reshape(a__  ,	a__)

			return x


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[Any] 							= x.shape[0]
			a_						:						Optional[int] 							= x.reshape(4  ,	in_channel // 4)
			a_						:						int 							= x[[0, 2, 1, 3], :].transpose(0  ,	1).reshape(a__)
			return x


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[Any] 							= x.shape[0]
			a_						:						Optional[int] 							= x.reshape(in_channel // 4  ,	4)
			a_						:						Optional[int] 							= x[:, [0, 2, 1, 3]].transpose(0  ,	1).reshape(a__)
			return x


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Any 							= {
			    """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
			    """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
			    """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
			    """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
			}
			a_						:						Tuple 							= model_name_to_url[model_name]
			a_						:						Optional[int] 							= torch.hub.load_state_dict_from_url(a__  ,	map_location="""cpu"""  ,	file_name=a__)[
			    """state_dict"""
			]

			for name, param in state_dict.items():
						print(a__  ,	param.shape)

			a_						:						str 							= get_upernet_config(a__)
			a_						:						List[str] 							= UperNetForSemanticSegmentation(a__)
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						a_						:						List[str] 							= state_dict.pop(a__)
						if "bn" in key:
									a_						:						Tuple 							= key.replace("""bn"""  ,	"""batch_norm""")
						a_						:						Union[str, Any] 							= val

			# rename keys
			a_						:						Optional[Any] 							= create_rename_keys(a__)
			for src, dest in rename_keys:
						rename_key(a__  ,	a__  ,	a__)
			read_in_q_k_v(a__  ,	config.backbone_config)

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												a_						:						Dict 							= reverse_correct_unfold_reduction_order(a__)
									if "norm" in key:
												a_						:						str 							= reverse_correct_unfold_norm_order(a__)

			model.load_state_dict(a__)

			# verify on image
			a_						:						List[Any] 							= """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
			a_						:						str 							= Image.open(requests.get(a__  ,	stream=a__).raw).convert("""RGB""")

			a_						:						Tuple 							= SegformerImageProcessor()
			a_						:						List[str] 							= processor(a__  ,	return_tensors="""pt""").pixel_values

			with torch.no_grad():
						a_						:						int 							= model(a__)
						a_						:						str 							= outputs.logits

			print(logits.shape)
			print("""First values of logits:"""  ,	logits[0, 0, :3, :3])
			# assert values
			if model_name == "upernet-swin-tiny":
						a_						:						Optional[int] 							= torch.tensor(
						    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]])
			elif model_name == "upernet-swin-small":
						a_						:						Union[str, Any] 							= torch.tensor(
						    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]])
			elif model_name == "upernet-swin-base":
						a_						:						Optional[Any] 							= torch.tensor(
						    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]])
			elif model_name == "upernet-swin-large":
						a_						:						int 							= torch.tensor(
						    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]])
			print("""Logits:"""  ,	outputs.logits[0, 0, :3, :3])
			assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,	a__  ,	atol=1e-4)
			print("""Looks ok!""")

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving processor to {pytorch_dump_folder_path}''')
						processor.save_pretrained(a__)

			if push_to_hub:
						print(f'''Pushing model and processor for {model_name} to hub''')
						model.push_to_hub(f'''openmmlab/{model_name}''')
						processor.push_to_hub(f'''openmmlab/{model_name}''')


if __name__ == "__main__":
	__snake_case				:      Optional[int]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""upernet-swin-tiny""",
	    type=str,
	    choices=[F"""upernet-swin-{size}""" for size in ["""tiny""", """small""", """base""", """large"""]],
	    help="""Name of the Swin + UperNet model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	__snake_case				:      Optional[Any]   		=			parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Union[str, Any]   		=			{
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''cvt'''





						def __init__(    self  ,      _lowercase=3  ,      _lowercase=[7, 3, 3]  ,      _lowercase=[4, 2, 2]  ,      _lowercase=[2, 1, 1]  ,      _lowercase=[64, 192, 384]  ,      _lowercase=[1, 3, 6]  ,      _lowercase=[1, 2, 10]  ,      _lowercase=[4.0, 4.0, 4.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.1]  ,      _lowercase=[True, True, True]  ,      _lowercase=[False, False, True]  ,      _lowercase=["dw_bn", "dw_bn", "dw_bn"]  ,      _lowercase=[3, 3, 3]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[2, 2, 2]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      **_lowercase  ,      )     -> Tuple:
									super().__init__(**_lowercase    )
									a_						:						str 							= num_channels
									a_						:						Optional[int] 							= patch_sizes
									a_						:						Dict 							= patch_stride
									a_						:						int 							= patch_padding
									a_						:						int 							= embed_dim
									a_						:						Tuple 							= num_heads
									a_						:						Optional[Any] 							= depth
									a_						:						str 							= mlp_ratio
									a_						:						Optional[int] 							= attention_drop_rate
									a_						:						int 							= drop_rate
									a_						:						List[Any] 							= drop_path_rate
									a_						:						List[Any] 							= qkv_bias
									a_						:						Dict 							= cls_token
									a_						:						Union[str, Any] 							= qkv_projection_method
									a_						:						List[Any] 							= kernel_qkv
									a_						:						Optional[Any] 							= padding_kv
									a_						:						Any 							= stride_kv
									a_						:						Dict 							= padding_q
									a_						:						Optional[int] 							= stride_q
									a_						:						Tuple 							= initializer_range
									a_						:						int 							= layer_norm_eps


